dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE (http://glx.sourceforge.net/) is installed.
Included in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples.
The most recent source code can be obtained from GitHub: <a href="https://github.com/baubie/dtnet">https://github.com/baubie/dtnet</a>
Reference: 1 .
Aubie B, Becker S, Faure PA (2009) Computational models of millisecond level duration tuning in neural circuits. J Neurosci29:9255-70 [PubMed]

More information and latest version available at:
https://www.github.com/baubie/dtnet
Requirements
============
- Boost 1.47+
- Readline
- GLE (For graphing)
Installation
============
$ ./configure
$ make
$ sudo make install
Options are available for configure to specify installation location
and library locations if configure is unable to find them on their
own. Type
./configure --help
for more information.
Post-Installation
=================
Before dtnet can be used, you MUST create a file called ~/.libdtnetrc
In this file, add the following line:
models=HH aEIF Poisson
This tells dtnet which neural models are available
Running Examples
================
Examples are included in the examples diretory to reproduce the
figures in Aubie et al. (2009). For example, to reproduce the figures
generated by bandpass.dtnet (Figures are documented within each .dtnet
file), simply enter the examples directory and run:
# dtnet -s bandpass.dtnet
Ensure GLE is installed and working in order for the figures to be
produced. If GLE throws errors or a seg fault occurs, ensure you have
adequate memory (at least 4GB of RAM is required for many
simulations).
Reference
=========
Aubie, Becker Faure (2009) Computational models of millisecond level
duration tuning in neural circuits. J Neurosci 29:9255-9270.